The evolutionary basis of human biological aging is not well understood. Basic theory suggests that aging should be opposed by natural selection as it causes decreased fertility and an increased risk of death, and is associated with major decreases in Darwinian fitness. Why then is aging a universal human trait? Why is it that all humans age? Several theories have proposed explanations for the existence of human aging. Major theories include: 1) the theory of mutation accumulation, and 2) the theory of antagonistic pleiotropy, with the theory of disposable soma as a special case. These theories are not mutually exclusive, and it is likely that all three explain some portion of the aging process. The size of the contribution each theory makes, however, is still unknown. Such information will help to clarify the roles that different types of genes play in the aging process. To help fill this gapin knowledge we will test predictions of evolutionary theories of aging using DNA methylation data as an innovative measure of aging. Genome-wide DNA methylations patterns have been shown to be highly dynamic with age, and are hypothesized to be reflective of the aging process. Longitudinal and familial DNA methylation data will allow us to test previously unexplored theory predictions related to aging as a lifelong process. The specific aims of the proposed research are to: 1) test whether the heritability of DNA methylation and rate of aging are consistent with disposable soma or mutation accumulation models, and 2) test whether DNA methylation data support a stochastic aging process implied by the disposable soma model. The approaches we propose to reach our specific aims represent novel extensions of existing methods of analysis and original formulations of mathematical models. To reach our first aim we will use a combination of established quantitative genetic methods to test specific hypotheses about the heritability of DNA methylation pattern changes and the rate of aging. To reach our second aim we will develop an original and testable stochastic model of DNA methylation changes throughout life.